The present invention relates to relational analysis and representation, database information retrieval and search engine technology and, more specifically, a system and method of analyzing data in context.
The vast amount of text and other types of information available in electronic form have contributed substantially to an xe2x80x9cinformation glut.xe2x80x9d In response, researchers are creating a variety of methods to address the need to efficiently access electronically stored information. Current methods are typically based on finding and exploiting patterns in collections of text. Variations among the methods and the factions are primarily due to varying allegiances to linguistics, quantitative analysis, representations of domain expertise, and the practical demands of the applications. Typical applications involve finding items of interest from large collections of text, having appropriate items routed to the correct people, and condensing the contents of many documents into a summary form.
One known application includes various forms of, and attempts to improve upon, keyword search type technologies. These improvements include statistical analysis and analysis based upon grammar or parts of speech. Statistical analysis generally relies upon the concept that common or often-repeated terms are of greater importance than less common or rarely used terms. Parts of speech attach importance to different terms based upon whether the term is a noun, verb, pronoun, adverb, adjective, article, etc. Typically a noun would have more importance than an article therefore nouns would be processed where articles would be ignored.
Other known methods of processing electronic information include various methods of retrieving text documents. One example is the work of Hawking, D. A. and Thistlewaite, P. B.: Proximity Operatorsxe2x80x94So Near And Yet So Far. In D. K. Harman, (ed.) Proc. Fourth Text Retrieval Conf. (TREC), pp 131-144, NIST Special Publication 500-236, 1996. Hawking, D. A. and Thistlewaite, P. B.: Relevance Weighting Using Distance Between Term Occurrences. Technical Report TR-CS-96-08, Department of Computer Science, Australian National University, June 1996 (Hawking and Thistlewaite (1995, 1996)) on the PADRE system.
The PADRE system applies complex proximity metrics to determine the relevance of documents. PADRE measures the spans of text that contain clusters of any number of target words. Thus, PADRE is based on complex, multi-way (xe2x80x9cN-aryxe2x80x9d) relations. PADRE""s spans and clusters have complex, non-intuitive, and somewhat arbitrary definitions. Each use of PADRE to rank documents requires a user to manually select and specify a small group of words that might be closely clustered in the text. PADRE relevance criteria are based on the assumption that the greatest relevance is achieved when all of the target words are closest to each other. PADRE relevance criteria are generated manually, by the user""s own xe2x80x9chuman free association.xe2x80x9d PADRE, therefore, is imprecise and often generates inaccurate search/comparison results.
Other prior art methods include various methodologies of data mining. See for example: Fayyad, U.; Piatetsky-Shapiro, G.; and Smyth, P: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Comm. ACM, vol. 39, no. 11, 1996, pp. 27-34 (Fayyad, et al., 1996). Search engines Zorn, P.; Emanoil, M.; Marshall, L; and Panek, M.: Advanced Web Searching: Tricks of the Trade. ONLINE, vol. 20, no. 3, 1996, pp. 14-28, (Zorn, et al., 1996). Discourse analysis Kitani, T.; Eriguchi, Y.; and Hara, M.: Pattern Matching and Discourse Processing in Information Extraction from Japanese Text. JAIR, vol. 2, 1994, pp. 89-100, (Kitani, et al., 1994). Information extraction Cowie, J. and Lehnert, W.: Information Extraction. Comm. ACM, vol. 39, no. 1, 1996, pp. 81-91, (Cowie, et al., 1996). Information filtering Foltz, P. W. and Dumais, S. T.: Personalized Information Deliveryxe2x80x94An Analysis of Information Filtering Methods. Comm. ACM, vol. 35, no. 12, 1992, pp. 51-60, (Foltz, et al., 1992). Information retrieval Salton, G.: Developments in Automatic Text Retrieval, Science, vol. 253, 1991, pp. 974-980, (Salton Developments . . . 1991) and digital libraries Fox, E. A.; Akscyn, R. M.; Furuta, R. K.; and Leggett, J. J.: Digital Librariesxe2x80x94Introduction. Comm. ACM., vol. 38, no. 4, pp. 22-28, 1995 (Fox, et al. 1995). Cutting across these approaches are concerns about how to subdivide words and collections of words into useful pieces, how to categorize the pieces, how to detect and utilize various relations among the pieces, and how transform the many pieces into a smaller number of representative pieces.
Most keyword search methods use term indexing such as used by Salton, G.: A blueprint for automatic indexing. ACM SIGIR Forum, vol. 16, no. 2, 1981. Reprinted in ACM SIGIR Forum, vol. 31, no. 1, 1997, pp. 23-36. (Salton, A blueprint . . . 1981), where a word list represents each document and internal query. As a consequence, given a keyword as a user query, these methods use merely the presence of the keyword in documents as the main criterion of relevance. Some methods such as Jing, Y. and Croft, W. B.: An Association Thesaurus for Information Retrieval. Technical Report 94-17, University of Massachusetts, 1994 (Jing and Croft, 1994); Gauch, S., and Wang, J.: Corpus analysis for TREC 5 query expansion. Proc. TREC 5, NIST SP 500-238, 1996, pp. 537-547 (Gauch and Wang, 1996); Xu, J., and Croft, W.: Query expansion using local and global document analysis. Proc. ACM SIGIR, 1996, pp. 4-11. (Xu and Croft, 1996); McDonald, J., Ogden, W., and Foltz, P.: Interactive information retrieval using term relationship networks. Proc. TREC 6, NIST SP 500-240, 1997, pp. 379-383 (McDonald, Ogden, and Foltz, 1997), utilize term associations to identify or display additional query keywords that are associated with the user-supplied keywords. This results in, xe2x80x9cquery driftxe2x80x9d. Query drift occurs when the additional query keywords retrieve documents that are poorly related or unrelated to the original keywords. Further, term index methods are ineffective in ranking documents on the basis of keywords in context.
In the proximity indexing method of Hawking and Thistlewaite (1996, 1996), a query consists of a user-identified collection of words. These query words are compared with the words in the documents of the database. The search method seeks documents containing length-limited sequences of words that contain subsets of the query words. Documents containing greater numbers of query words in shorter sequences of words are considered to have greater relevance. Further, as with other conventional term indexing schemes, the method of Hawking et al. allows a single query term to be used to identify documents containing the tern, but cannot rank the identified documents containing the single query term according to the relevance of the documents to the contexts of the single query term within each document.
Most phrase search and retrieval methods that currently exist, such as Fagan, J. L.: Experiments in automatic phrase indexing for document retrieval: A comparison of syntactic and non-syntactic methods. Ph.D. thesis TR87-868, Department of Computer Science, Cornell University, 1987 (Fagan (1987)); Croft, W. B., Turtle, H. R., and Lewis, D. D.: The use of phrases and structure queries in information retrieval. Proc. ACM SIGIR, 1991, pp. 32-45 (Croft, Turtle, and Lewis (1991)); Gey, F. C., and Chen, A.: Phrase discovery for English and cross-language retrieval at TREC 6. Proc. TREC 6, NIST SP 500-240, 1997, pp. 637-644 (Gey and Chen (1997); Gutwin, C., Paynter, G., Witten, I. H., Nevill-Manning, C., and Frank E.: Improving browsing in digital libraries with keyphrase indexes. TR 98-1, Computer Science Department, University of Saskatchewan, 1998 (Gutwin, Paynter, Witten, Nevill-Manning, and Frank (1998)); Jones, S., and Stavely, M.: Phrasier: A system for interactive document retrieval using keyphrases. Proc. ACM SIGIR, 1999, pp. 160-167 (Jones and Staveley (1999)), and Jing and Croft (1994) all treat query phrases as single terms, and typically rely on lists of key phrases that have been generated at some previous time, to represent each document. This approach allows little flexibility in matching query phrases with similar phrases in the text, and this approach requires that all possible phrases be identified in advance, typically using statistical or xe2x80x9cnatural language processingxe2x80x9d (NLP) methods.
NLP phrase search methods are subject to problems such as mistagging, as described by Fagan (1987). Statistical phrase search methods, such as in Turpin, A., and Moffat, A.: Statistical phrases for vector-space information retrieval. Proc. ACM SIGIR, 1999, pp. 309-310 (Turpin and Moffat (1999)), depend on phrase frequency, and therefore are ineffective in searching for most phrases because most phrases occur infrequently. Croft, Turtle, and Lewis (1991) also dismisses the concept of implicitly representing phrases as term associations. Further, the pair-wise association metric of Croft, Turtle, and Lewis (1991) does not include or suggest a measurement of degree or direction of word proximity. Instead, the association method of Croft, Turtle, and Lewis (1991) uses entire documents as the contextual scope, and considers any two words that occur in the same document as being related to the same extent that any other pair of words in the document are related.
There are several methods of displaying phrases contained in collections of text as a way to assist a user in domain analysis or query formulation and refinement. Known methods such as Godby, C. J.: Two techniques for the identification of phrases in full text. Annual Review of OCLC Research. Online Computer Library Center, Dublin, Ohio, 1994 (Godby (1994)); Normore, L., Bendig, M., and Godby, C. J.: WordView: Understanding words in context. Proc. Intell. User Interf., 1999, pp. 194 (Normore, Bendig, and Godby (1999)); Zamir, E., and Etzioni, E.: Grouper: A dynamic clustering interface to web search results. Proc. 8th International World Wide Web Conference (WWW8), 1999 (Zamir and Etzioni, (1999)); Gutwin, Paynter, Witten, Nevill-Manning, and Frank (1998); and Jones and Staveley (1999), maintain explicit and incomplete lists of phrases. Some phrase generation methods such as Church, K., Gale, W., Hanks, P., and Hindle, D.: Using statistics in lexical analysis. In U. Zernik (ed.), Lexical Acquisition: Using On-Line Resources To Build A Lexicon. Lawrence Earlbaum, Hillsdale, N.J., 1991 (Church, Gale, Hanks, and Hindle (1991)); Gey and Chen (1997); and Godby (1994), use contextual association to identify important word pairs, but do not identify longer phrases, or do not use the same associative method to identify phrases having more than two words. Some known methods such as Gelbart, D., and Smith, J. C.: Beyond boolean search: FLEXICON, a legal text-based intelligent system. Proc. ACM Artificial Intelligence and Law, 1991, pp. 225-234 (Gelbart and Smith (1991)); Gutwin, Paynter, Witten, Nevill-Manning, and Frank (1998); and Jones and Staveley (1999) rely on manual identification of phrases at a critical point in the process.
The xe2x80x9cnatural language processingxe2x80x9d (NLP) methods such as Godby (1994); Jing and Croft (1994); Gutwin, Paynter, Witten, Nevill-Manning, and Frank (1998); Jones and Staveley (1999); and de Lima, E. F., and Pedersen, J. O.: Phrase recognition and expansion for short, precision-biased queries based on a query log. Proc. ACM SIGIR, 1999, pp. 145-152 (de Lima and Pedersen (1999)), classify words by part of speech using grammatical taggers and apply a grammar-based set of allowable patterns. These methods typically remove all punctuation and stopwords as a preliminary step, and most then discover only simple or compound nouns leaving all other phrases unrecognizable.
Keyphind and Phrasier methods of Gutwin, Paynter, Witten, Nevill-Manning, and Frank (1998) and Jones and Staveley (1999), identify some of the phrases in sets of documents that are relevant to initial user queries, and require users to select among the identified phrases to refine subsequent searches. Keyphind and Phrasier then rely on Natural Language Processing (NLP) methods of grammatical tagging and require pre-existing lists of identifiable phrases. In addition, Keyphind and Phrasier apply very restrictive limits on usable phrases, which significantly reduces the number and types of phrases that can be identified in documents. Keyphind and Phrasier""s methods restrict the amount of phrase information available for determinations of document relevance.
In accordance with one aspect of the present invention, a keyterm search is a method of searching a database for subsets of the database that are relevant to an input query. First, a number of relational models of subsets of a database are provided. A query is then input. The query can include one or more keyterms. Next, a gleaning model of the query is created. The gleaning model of the query is then compared to each one of the relational models of subsets of the database. The identifiers of the relevant subsets are then output.